I stored the raw files on Github, so I used RCurl with Wehrley’s method that utilizes read.csv to the fullest. It’s one of the best ways I’ve found to read in data and also set data-types at the same time. He’s done a great job on that function. The dataset contains one ID variable, one response variable and ten predictor variables.
library(RCurl,quietly = T)
library(tidyverse,quietly = T)
library(ggplot2,quietly = T)
library(gridExtra,quietly = T)
library(Amelia,quietly = T)
library(beanplot,quietly = T)
library(caret,quietly = T)
library(stringr,quietly = T)
library(party, quietly = T)
# library(rattle, quietly = T)
readData <- function(path.name, file.name, column.types, missing.types) {
gurl <- paste(path.name,file.name,sep="")
download.file(gurl,file.name,method="curl",quiet = T)
tbl_df(read.csv(file.name,colClasses=column.types,
na.strings=missing.types))
}
Titanic.path <- "https://raw.githubusercontent.com/rsangole/Titanic/master/"
train.data.file <- "train.csv"
test.data.file <- "test.csv"
missing.types <- c("NA", "")
train.column.types <- c('integer', # PassengerId
'factor', # Survived
'factor', # Pclass
'character', # Name
'factor', # Sex
'numeric', # Age
'integer', # SibSp
'integer', # Parch
'character', # Ticket
'numeric', # Fare
'character', # Cabin
'factor' # Embarked
)
test.column.types <- train.column.types[-2] # # no Survived column in test.csv
train.raw <- readData(Titanic.path, train.data.file,train.column.types,missing.types)
test.raw <- readData(Titanic.path, test.data.file,test.column.types,missing.types)
prep_data <- function(D) {
if (!is.null(D$Survived)) {
D$Survived <- factor(D$Survived,
levels = c(1, 0),
labels = c('Survived', 'Dead'))
}
D$Pclass <- factor(D$Pclass,
levels = c(1, 2, 3),
labels = c('P1', 'P2', 'P3'))
D$PassengerId <- NULL
D
}
train.raw <- prep_data(train.raw)
test.raw <- prep_data(test.raw)
str(train.raw)Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 891 obs. of 11 variables:
$ Survived: Factor w/ 2 levels "Survived","Dead": 2 1 1 1 2 2 2 2 1 1 ...
$ Pclass : Factor w/ 3 levels "P1","P2","P3": 3 1 3 1 3 3 1 3 3 2 ...
$ Name : chr "Braund, Mr. Owen Harris" "Cumings, Mrs. John Bradley (Florence Briggs Thayer)" "Heikkinen, Miss. Laina" "Futrelle, Mrs. Jacques Heath (Lily May Peel)" ...
$ Sex : Factor w/ 2 levels "female","male": 2 1 1 1 2 2 2 2 1 1 ...
$ Age : num 22 38 26 35 35 NA 54 2 27 14 ...
$ SibSp : int 1 1 0 1 0 0 0 3 0 1 ...
$ Parch : int 0 0 0 0 0 0 0 1 2 0 ...
$ Ticket : chr "A/5 21171" "PC 17599" "STON/O2. 3101282" "113803" ...
$ Fare : num 7.25 71.28 7.92 53.1 8.05 ...
$ Cabin : chr NA "C85" NA "C123" ...
$ Embarked: Factor w/ 3 levels "C","Q","S": 3 1 3 3 3 2 3 3 3 1 ...
Quick investigation of missing values can be done using the complete.cases(), and more thorough graphical summary can be done using Amelia. Overall, 79% of the observations have some missing data.
#Complete cases (percentages)
round(prop.table(table(complete.cases(train.raw))),2)
FALSE TRUE
0.79 0.21
Amelia lets us graphically investigate which variables have missing data. purr::map_xxx() gives this same information numerically in a succint fashion.
missmap(train.raw, main='Missing Values Analysis using Amelia ordered by % missing', col=c('red', 'gray'),legend = F,rank.order = T)#Missing cases (numbers):
map_int(train.raw,~sum(is.na(.x)))Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin
0 0 0 0 177 0 0 0 0 687
Embarked
2
#Missing cases (percentages):
round(map_dbl(train.raw,~sum(is.na(.x))/length(.x)),2)Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin
0.00 0.00 0.00 0.00 0.20 0.00 0.00 0.00 0.00 0.77
Embarked
0.00
Cabin has a large number of missing values (77% missing). Imputing this variable may prove challenging or even useless. Age (19.9% missing) and Embarked (0.2%) missing are much more managable.
The first step in the analysis is to explore the data numerically and graphically. I always split up my EDA investigation as follows:
This gives me a structured approach towards larger datasets. My professor at Northwestern taught me to always complete a thorough intimate numeric & graphical EDA on the data, no matter how large the data 1. Anscombe (1973) clearly shows the importance of graphical analyses.
Survived is the response variable. As we can see, a large majority of the passengers did not survive the accident. The response variable is a False/True boolean variable. Thus, the analysis techniques used later will be those appropriate for classification problems.
round(prop.table(table(train.raw$Survived)),2)
Survived Dead
0.38 0.62
The first step is to look at every variable available. I prefer using the ggplot2 framework for all the visuals.
Age seems to have a bimodal distribution - very young children, and then directly young adults to mid-age persons. The 2nd mode is right skewed with no obvious outliers.
Fare certainly shows many outliers beyond the ~$200 level. A majority of the fares are <$50, which makes sense since a majority of the travelers are bound to be in the 3rd passenger class.
p1 <- ggplot(data=train.raw,aes(x=Age))+geom_histogram(bins = 40)
p2 <- ggplot(data=train.raw,aes(x=Fare))+geom_histogram(bins = 40)
grid.arrange(p1,p2)As we can see, the median fare is $14.5, the mean is $32, but the max is $512. We’ll investigate winzorising this variable in the latter part. Perhaps a transformation will also help?
summary(train.raw$Fare) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00 7.91 14.45 32.20 31.00 512.33
A ggplot command is iterated over for the categorical variables.2
Key takeways for the categorical variables:
Pclass: If you were traveling 1st class, you have the highest chance of survival. Could be indicative of preferential treatment to those who paid more, a less politically correct class-stratified society, as well as the fact that the 1st class passengers had cabins at the very top of the ship.Pclass: Persons traveling 3rd class had the highest fatality rate. 3rd class passengers had cabins deep in the ship. With the reasons give in (1), this could have contributed to the low survival rate.Sex: Males have a very high fatality rate. Seems like the ‘women and children’ first policy was followed during evacuation.SibSp & Parch: What’s interesting here is, for both these variables, at level 0, the fatality rate is higher. At levels 1+, the chances of survival are much better. Again, this could point to the ‘women and children’ policy being followed. (Or perhaps there weren’t as many families with children on board!)Embarked: Southampton has a higher fatality rate than Cherbourg or Queenstown. A cross-tabulation between Embarked and Pclass shows that 72% of the 3rd class passengers and 89% of the 2nd class passengers boarded at Southampton. This jives with the observation that 2nd and 3rd class passengers have higher fatality rates.get_legend<-function(myggplot){
tmp <- ggplot_gtable(ggplot_build(myggplot))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend <- tmp$grobs[[leg]]
return(legend)
}
p <- lapply(X = c('Pclass','Sex','SibSp','Parch','Embarked'),
FUN = function(x) ggplot(data = train.raw)+
aes_string(x=x,fill='Survived')+
geom_bar(position="dodge")+
theme(legend.position="none"))
legend <- get_legend(ggplot(data = train.raw,aes(x=Pclass,fill=Survived))+geom_bar())
grid.arrange(p[[1]],p[[2]],p[[3]],p[[4]],p[[5]],legend,layout_matrix =
cbind(c(1,2,3),c(4,5,NA),c(6,6,6)),widths=c(3,3,1))# round(prop.table(table(train.raw$Embarked,train.raw$Pclass),margin = 2),2)Grouped boxplots are a common method of comparing distributions grouped by categorical variables. I find beanplots to be excellent complementary plots to boxplots (and in some cases, even better). They’re a bit tricky to read at first - since they are so underutilized - but just through one plot, a wealth of information can be extracted.3
Here is a comparison of the same information between a boxplot and a beanplot. What can we infer from the bean plot better?
ggplot(train.raw,aes(y=Age,x=Pclass))+geom_boxplot(aes(fill=Survived))+theme_bw()beanplot(Age~Survived*Pclass,side='b',train.raw,col=list('yellow','orange'),
border = c('yellow2','darkorange'),ll = 0.05,boxwex = .5,
main='Passenger survival by pclass and Age',xlab='Passenger Class',ylab='Age')
legend('topright', fill = c('yellow','orange'), legend = c("Dead", "Survived"),bty = 'n',cex = .8)A look into the SibSp and Parch variables shows something interesting. There are three regions one can identify:
SibSp<=3 and Parch<=3, there are better chances for survival.The grouping by Pclass reveals that all the large families were 3rd class travelers. Worse access to help… lowest chance for survival.
These could be simple rules either hard coded during model building: something along the lines of: IF (SibSp>3 OR Parch >3) THEN prediction = 0, or some derived variables can be created.
ggplot(train.raw,aes(y=SibSp,x=Parch))+
geom_jitter(aes(color=Survived,shape=Pclass))+
theme_bw()+
scale_shape(solid=F)+
geom_vline(xintercept = 3,color='darkblue',lty=3)+
geom_hline(yintercept = 3,color='darkblue',lty=3)Starting with the easier one first:
Embarked: The largest portion of the passengers embared at Southhampton. I’m replacing the NAs with the same. First, I create a new imputed training dataset.
summary(train.raw$Embarked) C Q S NA's
168 77 644 2
train.imp <- train.raw
train.imp$Embarked[is.na(train.imp$Embarked)]='S'Names, Titles & Age:
The names have titles embedded in the strings. I can extract these using regex. Master, Miss, Mr and Mrs are the most popular - no surprise there, with lots of other titles. Here’s the distribution of the titles by age. These can be used to impute the missing age values.
train.raw$title <- str_extract(pattern = '[a-zA-Z]+(?=\\.)',string = train.raw$Name)
train.raw$title <- as.factor(train.raw$title)
train.raw %>%
na.omit() %>%
group_by(title) %>%
dplyr::summarise(Count=n(), Median_Age=round(median(Age),0)) %>%
arrange(-Median_Age)ggplot(train.raw,aes(x=title,y=Age))+
stat_summary(aes(y = Age,group=1), fun.y=median, colour="red", geom="point",group=1)+
geom_jitter(shape=21,alpha=.6,col='blue')+
theme_bw()+
theme(axis.text.x = element_text(angle = 45, hjust = 1),legend.position="none")+
labs(caption='Red points are median values')Grouping similar titles together, I’ve kept a few titles - Officer, Royalty, Mr, Mrs and Miss.
train.imp <- train.raw
train.imp$title <- as.character(train.imp$title)
train.imp$title[train.imp$title %in% c('Capt','Col','Major')] <- 'Officer'
train.imp$title[train.imp$title %in% c('Don','Dr','Rev','Sir','Jonkheer','Countess','Lady','Dona')] <- 'Royalty'
train.imp$title[train.imp$title %in% c('Mrs','Mme')] <- 'Mrs'
train.imp$title[train.imp$title %in% c('Ms','Mlle')] <- 'Miss'
train.imp$title <- as.factor(train.imp$title)
train.imp %>%
group_by(title) %>%
summarise(Median_Age=median(Age,na.rm = T))ggplot(train.imp,aes(x=title,y=Age))+
geom_jitter(shape=21,alpha=.6,col='blue')+
stat_summary(aes(y = Age,group=1), fun.y=median, colour="red", geom="point",group=1)+
theme_bw()+
theme(axis.text.x = element_text(angle = 45, hjust = 1),legend.position="none")+
labs(caption='Red points are median values')Now for the missing Age values. I’m trying out two strategies to impute age, just for kicks. First, a regression tree using the rpart method. 5-repeat 10-fold cross validation across a tuning grid of 20 values of maxdepth. RMSE stablizes at a depth of 14, with a value of 12.2.
age.predictors <- train.imp %>%
dplyr::select(-Survived,-Cabin,-Ticket,-Name) %>%
filter(complete.cases(.))
set.seed(1234)
ctrl <- trainControl(method = "boot",
repeats = 5,
number = 200
)
rpartGrid <- data.frame(maxdepth = seq(4,20,2))
rpartFit <- train(Age~.,
data=age.predictors,
method='rpart2',
trControl = ctrl,
tuneGrid = rpartGrid
)
rpartFitCART
712 samples
7 predictor
No pre-processing
Resampling: Bootstrapped (200 reps)
Summary of sample sizes: 712, 712, 712, 712, 712, 712, ...
Resampling results across tuning parameters:
maxdepth RMSE Rsquared
4 12.91352 0.2172555
6 12.56362 0.2600303
8 12.37466 0.2835666
10 12.28184 0.2961068
12 12.23967 0.3028092
14 12.23329 0.3046570
16 12.24043 0.3041673
18 12.23669 0.3045630
20 12.23821 0.3044234
RMSE was used to select the optimal model using the smallest value.
The final value used for the model was maxdepth = 14.
plot(rpartFit)plot(rpartFit$finalModel,margin=0.02)
text(rpartFit$finalModel,cex=0.8)Another way is to run a randomforest with a search over values of mtry using 5-repeat 10-fold cross validation. As we can see mtry=4 is the optimal value which results in the lowest RMSE of 11.4; much better than the rpart model.
set.seed(1234)
rfGrid <- data.frame(mtry=seq(1,6,1))
ctrl <- trainControl(method = "repeatedcv",
repeats = 5
)
rfFit <- train(Age~.,
data=age.predictors,
method='rf',
trControl = ctrl,
tuneGrid = rfGrid)
rfFitRandom Forest
712 samples
7 predictor
No pre-processing
Resampling: Cross-Validated (10 fold, repeated 5 times)
Summary of sample sizes: 642, 640, 642, 641, 640, 639, ...
Resampling results across tuning parameters:
mtry RMSE Rsquared
1 12.46449 0.3816241
2 11.33714 0.4192503
3 11.05166 0.4263448
4 11.04766 0.4217797
5 11.10717 0.4152716
6 11.20324 0.4066238
RMSE was used to select the optimal model using the smallest value.
The final value used for the model was mtry = 4.
plot(rfFit)I’m going to use the randomForest model. Using the predict.train() to predict values of age and plug them back into the imputed data. You can see the blue points which are the imputed values of Age. What I noticed is that for all the titles, the imputed Age value seems to be distributed fairly well, except Master. For Master, the three imputed are definitely outliers. I’m going to force these to the median Age.
missing.age <- train.imp %>% filter(is.na(Age))
age.predicted <- predict(rfFit, newdata = missing.age)
train.imp %>%
mutate(AgeMissing = is.na(Age),
Age = ifelse(AgeMissing,age.predicted,Age)) %>%
ggplot(aes(x=title,y=Age))+
stat_summary(aes(y = Age,group=1), fun.y=median, colour="red", geom="point",group=1)+
geom_jitter(aes(y=Age,col=AgeMissing),shape=2)+
theme_bw()+
theme(axis.text.x = element_text(angle = 45, hjust = 1),legend.position="none")+
labs(caption='Red points are median values')train.imp$Age[is.na(train.imp$Age)] <- age.predicted
train.imp$Age[train.imp$title=='Master' & train.imp$Age > 20] <- median(train.imp$Age[train.imp$title=='Master'],na.rm = T)Child?: Trying out two engineered variables here - is the passenger a child or not? Using Age=18 as a threshold. And is s/he close enough to be considered a adult by chance? Those between 16 and 18 could be mistaken for not being children. (My way of incorporating a fudge factor in the decision process of ladies & children first.)
train.imp$child <- 0
train.imp$child[train.imp$Age<18] <- 1
train.imp$almostadult <- as.numeric(between(train.imp$Age,16,18))Really young, or really old?: Really young ones and older folks would get priority perhaps. Creating two categorical binary variables for these conditions.
train.imp$Young <- ifelse(train.imp$Age<10,1,0)
train.imp$Seniors <- ifelse(train.imp$Age>60,1,0)Family related: Let’s also create some variables that talk about family sizes. What’s the total family size – continous variable TotalFam. Is the person single, part of a couple or a family? Three categorical variables for these.
train.imp$TotalFam <- train.imp$SibSp + train.imp$Parch + 1
# train.imp$LastName <- train.imp$Name %>% str_extract(pattern = '[a-zA-Z]+(?=,)')
# train.imp$FamSize <- paste0(train.imp$TotalFam,train.imp$LastName)
# train.imp$LastName <- NULL
train.imp$Name <- NULL
train.imp$LargeParCh <- as.numeric(train.imp$Parch>=3)
train.imp$LargeSibSp <- as.numeric(train.imp$SibSp>=3)
train.imp$Single <- ifelse(train.imp$TotalFam==1,1,0)
train.imp$Couple <- ifelse(train.imp$TotalFam==2,1,0)
train.imp$Family <- ifelse(train.imp$TotalFam>2,1,0)Cabin related: Extracting the cabin alphabet and number from the cabin variable. Since the cabin numbers could be ordered from left to right or top to bottom on the boat, perhaps only the 1st digit is significant. Also, some folks have more than 1 cabin. Wonder if that’s important. Since lots of unknowns in the Cabin variable, all NA values are replaced by ‘U’. Refering to the deck diagram, the topmost decks are A and B, which are closest to the lifeboats. Perhaps that’s important too. Here, I create a bunch of categorical variables based off the original Cabin, and then remove it from the dataset.
train.imp$CabinMissing <- as.numeric(is.na(train.raw$Cabin))
train.imp$CabinCode <- map_chr(train.raw$Cabin,~str_split(string = .x,pattern = '')[[1]][1])
train.imp$CabinCode[is.na(train.imp$CabinCode)] <- 'U'
train.imp$CabinNum <- as.numeric(map_chr(train.raw$Cabin,~str_split(string = .x,pattern = '[a-zA-Z]')[[1]][2]))
train.imp$CabinNum <- map_int(train.imp$CabinNum, ~as.integer(str_split(.x,pattern = '',simplify = T)[1][1]))
train.imp$CabinNum[is.na(train.imp$CabinNum)] <- 0
train.imp$TopDeck <- ifelse(train.imp$CabinCode %in% c('A','B'),1,0)
train.imp$MidDeck <- ifelse(train.imp$CabinCode %in% c('C','D'),1,0)
train.imp$LowerDeck <- ifelse(train.imp$TopDeck==0 & train.imp$MidDeck ==0 ,1,0)
train.imp$NumberofCabins <- map_int(train.raw$Cabin,~str_split(string = .x,pattern = ' ')[[1]] %>% length)
train.imp$Cabin <- NULLTicket: Lastly, the ticket variable. I’m not sure what to make of it, so I’m keeping it for now, after cleaning it up a bit. A majority (80%) of the rows have unique (one) ticket. 14% rows have a duplicate ticket, perhaps indicating a family. A small number of rows have 3+ duplicates of the tickets.
train.imp$Ticket %>% table() %>% as.numeric() %>% table().
1 2 3 4 5 6 7
547 94 21 11 2 3 3
There seems to be a bit of a pattern here. Tickets starting with 1 are mostly 1st class, those starting with 2 are 2nd class, and 3 - 3rd class. But, I feel it’s a very loose association.
train.imp %>% group_by(Pclass) %>% dplyr::select(Ticket,Pclass) %>% sample_n(5)What I’m going to do is clean up the columns (remove special characters, spaces etc), then split the Ticket column into four: TicketChar, TicketNum,TicketNumLength, TicketNumStart. (Upon running the script a few times, I’ve decided to get rid of TicketNum, but I’m commenting the code for future ref). The TicketChar variable as this distribution:
train.imp %<>%
mutate(
Ticket = str_to_upper(Ticket) %>%
str_replace_all(pattern = regex(pattern = '[.\\/]'),replacement = ''),
TicketNum = str_extract(Ticket,pattern = regex('([0-9]){3,}')),
TicketNumStart = map_int(TicketNum,~as.integer(str_split(.x,pattern = '',simplify = T)[1])),
TicketNumLen = map_int(TicketNum,~dim(str_split(.x,pattern = '',simplify = T))[2]),
TicketChar = str_extract(Ticket,pattern = regex('^[a-zA-Z/\\.]+'))
) %>%
mutate(
TicketChar = map_chr(.x=TicketChar,
.f=~str_split(string=.x, pattern = '',simplify = T)[1])
) %>%
mutate(
TicketChar = ifelse(is.na(TicketChar),'U',TicketChar),
TicketNumStart = ifelse(is.na(TicketNumStart),0,TicketNumStart),
TicketNumLen = ifelse(is.na(TicketNumLen),0,TicketNumLen),
)
train.imp$Ticket <- NULL
train.imp$TicketNum <- NULL
table(train.imp$TicketChar)
A C F L P S U W
29 47 7 4 65 65 661 13
table(train.imp$TicketNumLen)
1 3 4 5 6 7
6 7 165 246 423 44
table(train.imp$TicketNumStart)
0 1 2 3 4 5 6 7 8 9
6 231 230 365 15 9 14 15 3 3
The fare variable has one massive outlier. Winzorising this variable using the 95th percentile value as the cutoff.
ggplot(train.imp,aes(x=Fare,fill=Pclass))+geom_histogram()+facet_grid(Pclass~.)quantile(train.imp$Fare[train.imp$Pclass=='P1'],probs = c(.1,.25,.5,.75,.95)) 10% 25% 50% 75% 95%
26.55000 30.92395 60.28750 93.50000 232.52395
train.imp$Fare[train.imp$Fare>232] <- 232The dataset is now prepared for modeling. Here’s a quick review of the data so far. 29 variables in total.
train.imp %>% glimpse()Observations: 891
Variables: 29
$ Survived <fctr> Dead, Survived, Survived, Survived, Dead, Dead, Dead, Dead, Survive...
$ Pclass <fctr> P3, P1, P3, P1, P3, P3, P1, P3, P3, P2, P3, P1, P3, P3, P3, P2, P3,...
$ Sex <fctr> male, female, female, female, male, male, male, male, female, femal...
$ Age <dbl> 22.00000, 38.00000, 26.00000, 35.00000, 35.00000, 35.04936, 54.00000...
$ SibSp <int> 1, 1, 0, 1, 0, 0, 0, 3, 0, 1, 1, 0, 0, 1, 0, 0, 4, 0, 1, 0, 0, 0, 0,...
$ Parch <int> 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 1, 0, 0, 5, 0, 0, 1, 0, 0, 0, 0, 0, 0,...
$ Fare <dbl> 7.2500, 71.2833, 7.9250, 53.1000, 8.0500, 8.4583, 51.8625, 21.0750, ...
$ Embarked <fctr> S, C, S, S, S, Q, S, S, S, C, S, S, S, S, S, S, Q, S, S, C, S, S, Q...
$ title <fctr> Mr, Mrs, Miss, Mrs, Mr, Mr, Mr, Master, Mrs, Mrs, Miss, Miss, Mr, M...
$ child <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1,...
$ almostadult <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ Young <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,...
$ Seniors <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ TotalFam <dbl> 2, 2, 1, 2, 1, 1, 1, 5, 3, 2, 3, 1, 1, 7, 1, 1, 6, 1, 2, 1, 1, 1, 1,...
$ LargeParCh <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ LargeSibSp <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,...
$ Single <dbl> 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1,...
$ Couple <dbl> 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,...
$ Family <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,...
$ CabinMissing <dbl> 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,...
$ CabinCode <chr> "U", "C", "U", "C", "U", "U", "E", "U", "U", "U", "G", "C", "U", "U"...
$ CabinNum <dbl> 0, 8, 0, 1, 0, 0, 4, 0, 0, 0, 6, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0,...
$ TopDeck <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ MidDeck <dbl> 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,...
$ LowerDeck <dbl> 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,...
$ NumberofCabins <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
$ TicketNumStart <dbl> 2, 1, 3, 1, 3, 3, 1, 3, 3, 2, 9, 1, 2, 3, 3, 2, 3, 2, 3, 2, 2, 2, 3,...
$ TicketNumLen <int> 5, 5, 7, 6, 6, 6, 5, 6, 6, 6, 4, 6, 4, 6, 6, 6, 6, 6, 6, 4, 6, 6, 6,...
$ TicketChar <chr> "A", "P", "S", "U", "U", "U", "U", "U", "U", "U", "P", "U", "A", "U"...
I’m experimenting with a few modeling techniques, mainly xgboost, gbm, and penalized models using glmnet. I’ve implemented all these models using caret which I find an absolutely indispensible toolkit to prep, build, tune and explore numerous models using very few lines of code.
For all models, I’m using a 5-repeat 10-fold cross validation technique on the training dataset. Thus, I have not split the training dataset further into test-train sets, given the small number of observations in the dataset.
Furthermore, given the 80:20 class-imbalance, I’m also trying out smote as an class balancing technique for a few models.
Tuning parameter searches (aka hypertuning) is performed using the tuneGrid parameter in the train() call. The best model is selected using the AUC of the ROC. Here are the models and a few intermediate results for each model. At the end, I’ve compared the performance of all the models together.
xgbFiteXtreme Gradient Boosting
891 samples
53 predictor
2 classes: 'Survived', 'Dead'
No pre-processing
Resampling: Cross-Validated (10 fold, repeated 5 times)
Summary of sample sizes: 801, 803, 802, 802, 802, 801, ...
Resampling results across tuning parameters:
eta max_depth nrounds ROC Sens Spec
0.3 2 2 0.8581505 0.6148067 0.9052660
0.3 2 3 0.8677307 0.7174454 0.8699394
0.3 2 4 0.8679025 0.7310084 0.8728485
0.3 2 5 0.8704769 0.7262689 0.8768687
0.3 2 6 0.8712057 0.7315630 0.8710303
0.3 2 7 0.8718286 0.7338655 0.8706667
0.3 3 2 0.8737598 0.7373782 0.8702963
0.3 3 3 0.8771590 0.7403025 0.8750303
0.3 3 4 0.8790200 0.7339160 0.8775623
0.3 3 5 0.8804902 0.7333109 0.8815690
0.3 3 6 0.8806026 0.7350420 0.8808620
0.3 3 7 0.8807379 0.7256975 0.8856027
0.3 4 2 0.8768955 0.7425882 0.8757710
0.3 4 3 0.8805986 0.7373277 0.8794141
0.3 4 4 0.8816917 0.7432101 0.8801145
0.3 4 5 0.8826447 0.7444538 0.8819259
0.3 4 6 0.8815038 0.7402185 0.8812121
0.3 4 7 0.8804470 0.7273109 0.8833872
0.3 5 2 0.8760521 0.7307059 0.8706397
0.3 5 3 0.8774392 0.7290084 0.8706532
0.3 5 4 0.8802163 0.7325210 0.8797845
0.3 5 5 0.8793293 0.7395294 0.8775960
0.3 5 6 0.8806162 0.7360336 0.8797778
0.3 5 7 0.8828496 0.7377143 0.8797778
0.3 6 2 0.8765941 0.7447059 0.8673535
0.3 6 3 0.8772791 0.7359832 0.8706599
0.3 6 4 0.8809166 0.7342857 0.8819057
0.3 6 5 0.8833376 0.7430084 0.8804646
0.3 6 6 0.8819891 0.7401008 0.8837508
0.3 6 7 0.8839215 0.7388908 0.8844916
0.3 7 2 0.8762051 0.7331092 0.8761010
0.3 7 3 0.8775272 0.7423866 0.8812189
0.3 7 4 0.8798993 0.7394286 0.8830370
0.3 7 5 0.8803574 0.7435798 0.8852323
0.3 7 6 0.8816307 0.7423866 0.8855960
0.3 7 7 0.8818346 0.7429748 0.8918047
0.5 2 2 0.8575588 0.6287899 0.8965320
0.5 2 3 0.8669722 0.7303529 0.8714074
0.5 2 4 0.8700492 0.7315126 0.8680943
0.5 2 5 0.8704436 0.7315126 0.8721145
0.5 2 6 0.8750264 0.7408571 0.8721145
0.5 2 7 0.8764070 0.7454790 0.8786734
0.5 3 2 0.8757504 0.7373950 0.8684714
0.5 3 3 0.8780924 0.7397479 0.8695556
0.5 3 4 0.8770632 0.7216134 0.8885320
0.5 3 5 0.8781472 0.7327059 0.8819663
0.5 3 6 0.8763049 0.7367227 0.8866734
0.5 3 7 0.8769986 0.7308235 0.8874209
0.5 4 2 0.8791924 0.7420000 0.8794074
0.5 4 3 0.8805028 0.7425714 0.8739327
0.5 4 4 0.8797489 0.7337983 0.8808889
0.5 4 5 0.8792675 0.7314454 0.8856027
0.5 4 6 0.8802721 0.7325042 0.8855960
0.5 4 7 0.8808258 0.7366218 0.8866936
0.5 5 2 0.8752125 0.7318319 0.8721010
0.5 5 3 0.8778833 0.7303025 0.8775690
0.5 5 4 0.8802324 0.7336807 0.8823030
0.5 5 5 0.8792583 0.7296471 0.8819394
0.5 5 6 0.8810007 0.7249412 0.8870438
0.5 5 7 0.8802394 0.7249244 0.8844916
0.5 6 2 0.8732269 0.7353613 0.8706465
0.5 6 3 0.8761028 0.7377143 0.8801347
0.5 6 4 0.8777128 0.7335462 0.8837845
0.5 6 5 0.8791022 0.7341176 0.8841684
0.5 6 6 0.8782975 0.7341176 0.8899731
0.5 6 7 0.8771589 0.7323866 0.8892458
0.5 7 2 0.8743506 0.7377815 0.8746330
0.5 7 3 0.8772219 0.7419160 0.8874007
0.5 7 4 0.8778936 0.7406723 0.8888687
0.5 7 5 0.8804557 0.7441513 0.8903300
0.5 7 6 0.8815295 0.7429580 0.8852189
0.5 7 7 0.8810468 0.7377311 0.8877576
Tuning parameter 'gamma' was held constant at a value of 1
Tuning parameter
was held constant at a value of 1
Tuning parameter 'subsample' was held constant at a value
of 1
ROC was used to select the optimal model using the largest value.
The final values used for the model were nrounds = 7, max_depth = 6, eta = 0.3, gamma =
1, colsample_bytree = 1, min_child_weight = 1 and subsample = 1.
plot(xgbFit)
xgb.importance(feature_names = colnames(Dtrain),model = xgbFit$finalModel) %>%
xgb.ggplot.importance()densityplot(xgbFit,pch='|')
predict(xgbFit,type = 'prob') -> train.Probs
histogram(~Survived+Dead,train.Probs)ctrl <- trainControl(method = "repeatedcv",
repeats = 5,
verboseIter = T,
classProbs = TRUE,
summaryFunction = twoClassSummary,
sampling = 'smote'
)
xgbGrid <- expand.grid(
nrounds=c(2,3,4,5,6,7),
max_depth=c(2,3,4,5,6,7),
eta=c(0.3,0.5),
gamma=1,
colsample_bytree=1,
min_child_weight=1,
subsample=1
)
dumV <- dummyVars(formula = Survived~.,data = train.imp)
Dtrain <- predict(dumV,train.imp)variable 'Survived' is not a factor
xgbsmoteFit <- train(
x=Dtrain,
y=train.imp$Survived,
method = 'xgbTree',
trControl = ctrl,
# metric = "Kappa",
tuneGrid = xgbGrid,
verbose = TRUE
)The metric "Accuracy" was not in the result set. ROC will be used instead.
+ Fold01.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
Attaching package: ‘DMwR’
The following object is masked from ‘package:plyr’:
join
- Fold01.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
Aggregating results
Selecting tuning parameters
Fitting nrounds = 7, max_depth = 6, eta = 0.3, gamma = 1, colsample_bytree = 1, min_child_weight = 1, subsample = 1 on full training set
xgbsmoteFiteXtreme Gradient Boosting
891 samples
53 predictor
2 classes: 'Survived', 'Dead'
No pre-processing
Resampling: Cross-Validated (10 fold, repeated 5 times)
Summary of sample sizes: 802, 802, 802, 802, 802, 802, ...
Addtional sampling using SMOTE
Resampling results across tuning parameters:
eta max_depth nrounds ROC Sens Spec
0.3 2 2 0.8422230 0.6017983 0.9041886
0.3 2 3 0.8492899 0.6334454 0.9001886
0.3 2 4 0.8559285 0.6701345 0.8968822
0.3 2 5 0.8626094 0.6590252 0.9031044
0.3 2 6 0.8654252 0.6521345 0.9060135
0.3 2 7 0.8660548 0.6718824 0.9016296
0.3 3 2 0.8594054 0.6228067 0.9235017
0.3 3 3 0.8638679 0.5971429 0.9336768
0.3 3 4 0.8659124 0.6034622 0.9326263
0.3 3 5 0.8663275 0.6007563 0.9300404
0.3 3 6 0.8692959 0.6066218 0.9307879
0.3 3 7 0.8724934 0.6129412 0.9256700
0.3 4 2 0.8552456 0.5870756 0.9348148
0.3 4 3 0.8626897 0.5895462 0.9337104
0.3 4 4 0.8694388 0.5895294 0.9358923
0.3 4 5 0.8729766 0.5995126 0.9362559
0.3 4 6 0.8755678 0.6064370 0.9366128
0.3 4 7 0.8794687 0.6110252 0.9388081
0.3 5 2 0.8600815 0.5929748 0.9329832
0.3 5 3 0.8633800 0.5917983 0.9311515
0.3 5 4 0.8689604 0.6000000 0.9347946
0.3 5 5 0.8729573 0.6028739 0.9377104
0.3 5 6 0.8751460 0.6105042 0.9340673
0.3 5 7 0.8766301 0.6169580 0.9333199
0.3 6 2 0.8633920 0.6239664 0.9191380
0.3 6 3 0.8673741 0.6320504 0.9205859
0.3 6 4 0.8715988 0.6268235 0.9231178
0.3 6 5 0.8750872 0.6378992 0.9220471
0.3 6 6 0.8761488 0.6419832 0.9209495
0.3 6 7 0.8800014 0.6496134 0.9169630
0.3 7 2 0.8561804 0.6192269 0.9195017
0.3 7 3 0.8610940 0.6321008 0.9162222
0.3 7 4 0.8646876 0.6397143 0.9176970
0.3 7 5 0.8695185 0.6490924 0.9129428
0.3 7 6 0.8710938 0.6572269 0.9143973
0.3 7 7 0.8713630 0.6619496 0.9143906
0.5 2 2 0.8490018 0.6370252 0.9012323
0.5 2 3 0.8599325 0.6549748 0.9038047
0.5 2 4 0.8629421 0.6410756 0.9125320
0.5 2 5 0.8664901 0.6181849 0.9194815
0.5 2 6 0.8674602 0.6101176 0.9202424
0.5 2 7 0.8699278 0.6131933 0.9238451
0.5 3 2 0.8610398 0.6136471 0.9242290
0.5 3 3 0.8634444 0.5919160 0.9347744
0.5 3 4 0.8695581 0.6042521 0.9296700
0.5 3 5 0.8712536 0.5908571 0.9289495
0.5 3 6 0.8726467 0.5941681 0.9347946
0.5 3 7 0.8752367 0.6024370 0.9351515
0.5 4 2 0.8626870 0.6024706 0.9329966
0.5 4 3 0.8693973 0.5964034 0.9347879
0.5 4 4 0.8755566 0.6059496 0.9315219
0.5 4 5 0.8759294 0.6204874 0.9373401
0.5 4 6 0.8739551 0.6158824 0.9373401
0.5 4 7 0.8744622 0.6292269 0.9318855
0.5 5 2 0.8631468 0.6064874 0.9278923
0.5 5 3 0.8709386 0.6163361 0.9282357
0.5 5 4 0.8742147 0.6315126 0.9267879
0.5 5 5 0.8749953 0.6338992 0.9271582
0.5 5 6 0.8747302 0.6426723 0.9238788
0.5 5 7 0.8757683 0.6466891 0.9191515
0.5 6 2 0.8625161 0.6239832 0.9213199
0.5 6 3 0.8671210 0.6332605 0.9184108
0.5 6 4 0.8676388 0.6419496 0.9111246
0.5 6 5 0.8689798 0.6571765 0.9154949
0.5 6 6 0.8728196 0.6530756 0.9125926
0.5 6 7 0.8742729 0.6594958 0.9129495
0.5 7 2 0.8649452 0.6513950 0.9213064
0.5 7 3 0.8670273 0.6544034 0.9209562
0.5 7 4 0.8748885 0.6684370 0.9198855
0.5 7 5 0.8736510 0.6684034 0.9176835
0.5 7 6 0.8763594 0.6748403 0.9180337
0.5 7 7 0.8767229 0.6777983 0.9154949
Tuning parameter 'gamma' was held constant at a value of 1
Tuning parameter
was held constant at a value of 1
Tuning parameter 'subsample' was held constant at a value
of 1
ROC was used to select the optimal model using the largest value.
The final values used for the model were nrounds = 7, max_depth = 6, eta = 0.3, gamma =
1, colsample_bytree = 1, min_child_weight = 1 and subsample = 1.
plot(xgbsmoteFit)xgb.importance(feature_names = colnames(Dtrain),model = xgbsmoteFit$finalModel)xgb.importance(feature_names = colnames(Dtrain),model = xgbsmoteFit$finalModel) %>%
xgb.ggplot.importance()densityplot(xgbsmoteFit,pch='|')predict(xgbsmoteFit,type = 'raw') -> train.Class
predict(xgbsmoteFit,type = 'prob') -> train.Probs
histogram(~Survived+Dead,train.Probs)boostFitStochastic Gradient Boosting
891 samples
53 predictor
2 classes: 'Survived', 'Dead'
No pre-processing
Resampling: Cross-Validated (10 fold, repeated 5 times)
Summary of sample sizes: 803, 802, 802, 802, 802, 802, ...
Resampling results across tuning parameters:
shrinkage interaction.depth n.trees ROC Sens Spec
0.01 1 500 0.8687740 0.7176975 0.8812727
0.01 1 700 0.8715465 0.7310756 0.8827340
0.01 1 900 0.8731344 0.7456975 0.8798182
0.01 1 1100 0.8737417 0.7614454 0.8790909
0.01 2 500 0.8768379 0.7410252 0.8819933
0.01 2 700 0.8781806 0.7549916 0.8808956
0.01 2 900 0.8791223 0.7614118 0.8801751
0.01 2 1100 0.8801043 0.7613950 0.8809024
0.01 3 500 0.8790775 0.7526218 0.8823636
0.01 3 700 0.8808075 0.7584874 0.8812727
0.01 3 900 0.8815746 0.7549748 0.8809091
0.01 3 1100 0.8817104 0.7532269 0.8849091
0.10 1 500 0.8699484 0.7480168 0.8721751
0.10 1 700 0.8708636 0.7485882 0.8736162
0.10 1 900 0.8686151 0.7422017 0.8743367
0.10 1 1100 0.8689881 0.7428067 0.8725051
0.10 2 500 0.8801393 0.7473950 0.8837845
0.10 2 700 0.8799571 0.7467563 0.8819798
0.10 2 900 0.8790711 0.7438992 0.8790370
0.10 2 1100 0.8773468 0.7474118 0.8757778
0.10 3 500 0.8803987 0.7450252 0.8790774
0.10 3 700 0.8805280 0.7438655 0.8746869
0.10 3 900 0.8793306 0.7438655 0.8692189
0.10 3 1100 0.8785908 0.7432773 0.8713872
Tuning parameter 'n.minobsinnode' was held constant at a value of 10
ROC was used to select the optimal model using the largest value.
The final values used for the model were n.trees = 1100, interaction.depth = 3, shrinkage
= 0.01 and n.minobsinnode = 10.
plot(boostFit)xyplot(oobag.improve~1:1100,data=boostFit$finalModel,alpha=.5,xlab = 'n.trees')plot(varImp(boostFit))densityplot(boostFit,pch='|')predict(boostFit,type = 'prob') -> train.Probs
histogram(~Survived+Dead,train.Probs)rfFit.yRandom Forest
891 samples
28 predictor
2 classes: 'Survived', 'Dead'
No pre-processing
Resampling: Cross-Validated (10 fold, repeated 5 times)
Summary of sample sizes: 803, 802, 802, 802, 802, 802, ...
Resampling results across tuning parameters:
mtry ROC Sens Spec
5 0.8842523 0.7122353 0.8969293
10 0.8885360 0.7286891 0.8940202
15 0.8874201 0.7351429 0.8878182
20 0.8864149 0.7415462 0.8867138
25 0.8866698 0.7427563 0.8816229
ROC was used to select the optimal model using the largest value.
The final value used for the model was mtry = 10.
plot(rfFit.y)plot(rfFit.y$finalModel)densityplot(rfFit.y,pch='|')predict(rfFit.y,type = 'prob') -> train.rf.Probs
histogram(~Survived+Dead,train.rf.Probs)rfsmoteFit.yRandom Forest
891 samples
28 predictor
2 classes: 'Survived', 'Dead'
No pre-processing
Resampling: Cross-Validated (10 fold, repeated 5 times)
Summary of sample sizes: 803, 802, 802, 802, 802, 802, ...
Addtional sampling using SMOTE
Resampling results across tuning parameters:
mtry ROC Sens Spec
5 0.8783818 0.6333782 0.9369966
10 0.8821756 0.6854286 0.9129697
15 0.8831274 0.6983025 0.9016498
20 0.8814043 0.7129580 0.8910976
25 0.8793606 0.7135462 0.8885455
ROC was used to select the optimal model using the largest value.
The final value used for the model was mtry = 15.
plot(rfsmoteFit.y)plot(rfsmoteFit.y$finalModel)densityplot(rfsmoteFit.y,pch='|')predict(rfsmoteFit.y,type = 'prob') -> train.rfsmoteFit.Probs
histogram(~Survived+Dead,train.rfsmoteFit.Probs)glmnetFitglmnet
891 samples
53 predictor
2 classes: 'Survived', 'Dead'
No pre-processing
Resampling: Cross-Validated (10 fold, repeated 5 times)
Summary of sample sizes: 803, 802, 802, 802, 802, 802, ...
Addtional sampling using SMOTE
Resampling results across tuning parameters:
alpha lambda ROC Sens Spec
0.0 0.01000000 0.8624351 0.7269580 0.8875152
0.0 0.01487179 0.8624351 0.7269580 0.8875152
0.0 0.01974359 0.8624351 0.7269580 0.8875152
0.0 0.02461538 0.8624351 0.7269580 0.8875152
0.0 0.02948718 0.8624351 0.7269580 0.8875152
0.0 0.03435897 0.8626287 0.7269580 0.8871515
0.0 0.03923077 0.8629192 0.7269412 0.8871515
0.0 0.04410256 0.8631763 0.7269076 0.8860539
0.0 0.04897436 0.8633421 0.7263361 0.8856902
0.0 0.05384615 0.8631602 0.7246218 0.8860539
0.0 0.05871795 0.8632236 0.7234286 0.8856902
0.0 0.06358974 0.8634279 0.7240168 0.8853266
0.0 0.06846154 0.8634157 0.7240168 0.8838721
0.0 0.07333333 0.8634152 0.7246218 0.8835017
0.0 0.07820513 0.8633267 0.7252269 0.8838653
0.0 0.08307692 0.8633870 0.7246723 0.8842290
0.0 0.08794872 0.8632672 0.7264370 0.8838653
0.0 0.09282051 0.8630221 0.7258487 0.8838653
0.0 0.09769231 0.8630112 0.7246723 0.8835017
0.0 0.10256410 0.8630108 0.7258487 0.8827744
0.0 0.10743590 0.8629793 0.7270252 0.8820337
0.0 0.11230769 0.8629057 0.7275966 0.8820337
0.0 0.11717949 0.8629160 0.7264370 0.8816700
0.0 0.12205128 0.8629367 0.7252605 0.8816700
0.0 0.12692308 0.8628505 0.7246723 0.8813064
0.0 0.13179487 0.8627742 0.7252437 0.8813064
0.0 0.13666667 0.8626347 0.7240840 0.8816700
0.0 0.14153846 0.8626660 0.7234790 0.8816700
0.0 0.14641026 0.8624861 0.7228908 0.8816700
0.0 0.15128205 0.8623359 0.7205378 0.8809360
0.0 0.15615385 0.8622288 0.7211261 0.8805724
0.0 0.16102564 0.8621643 0.7211092 0.8798451
0.0 0.16589744 0.8620677 0.7199496 0.8791111
0.0 0.17076923 0.8620130 0.7193782 0.8787475
0.0 0.17564103 0.8620221 0.7193782 0.8791111
0.0 0.18051282 0.8620338 0.7193782 0.8791111
0.0 0.18538462 0.8620142 0.7193782 0.8780202
0.0 0.19025641 0.8620445 0.7182017 0.8765657
0.0 0.19512821 0.8619043 0.7170252 0.8765657
0.0 0.20000000 0.8618487 0.7152773 0.8758384
0.1 0.01000000 0.8600372 0.7241176 0.8878519
0.1 0.01487179 0.8615477 0.7264538 0.8896768
0.1 0.01974359 0.8624891 0.7241345 0.8875017
0.1 0.02461538 0.8631904 0.7223866 0.8878653
0.1 0.02948718 0.8635738 0.7270252 0.8871246
0.1 0.03435897 0.8640134 0.7281513 0.8867542
0.1 0.03923077 0.8642901 0.7257983 0.8860269
0.1 0.04410256 0.8644447 0.7257983 0.8852997
0.1 0.04897436 0.8644893 0.7246218 0.8852997
0.1 0.05384615 0.8647542 0.7234622 0.8845724
0.1 0.05871795 0.8648624 0.7205378 0.8849360
0.1 0.06358974 0.8646418 0.7205378 0.8849360
0.1 0.06846154 0.8645368 0.7211261 0.8849360
0.1 0.07333333 0.8643896 0.7193950 0.8842020
0.1 0.07820513 0.8643594 0.7176303 0.8842020
0.1 0.08307692 0.8642748 0.7141008 0.8845589
0.1 0.08794872 0.8640778 0.7135294 0.8841953
0.1 0.09282051 0.8636771 0.7123697 0.8834815
0.1 0.09769231 0.8635362 0.7129580 0.8823906
0.1 0.10256410 0.8631628 0.7123697 0.8805657
0.1 0.10743590 0.8628222 0.7123529 0.8798316
0.1 0.11230769 0.8625770 0.7106050 0.8794680
0.1 0.11717949 0.8622052 0.7111933 0.8780135
0.1 0.12205128 0.8619550 0.7111933 0.8772795
0.1 0.12692308 0.8616472 0.7100336 0.8765522
0.1 0.13179487 0.8616144 0.7088908 0.8761886
0.1 0.13666667 0.8614748 0.7083025 0.8747340
0.1 0.14153846 0.8612211 0.7082857 0.8747340
0.1 0.14641026 0.8611024 0.7076975 0.8743636
0.1 0.15128205 0.8609678 0.7065210 0.8740000
0.1 0.15615385 0.8605709 0.7059328 0.8732727
0.1 0.16102564 0.8604414 0.7059496 0.8725455
0.1 0.16589744 0.8602385 0.7048067 0.8721751
0.1 0.17076923 0.8599341 0.7036471 0.8725387
0.1 0.17564103 0.8598033 0.7042353 0.8718114
0.1 0.18051282 0.8595210 0.7036639 0.8707205
0.1 0.18538462 0.8591656 0.7024874 0.8703569
0.1 0.19025641 0.8589091 0.7018992 0.8699933
0.1 0.19512821 0.8584617 0.7013277 0.8703569
0.1 0.20000000 0.8581420 0.7013277 0.8692660
0.2 0.01000000 0.8591220 0.7280672 0.8882020
0.2 0.01487179 0.8604820 0.7245882 0.8903906
0.2 0.01974359 0.8618651 0.7228571 0.8911178
0.2 0.02461538 0.8620406 0.7228908 0.8892997
0.2 0.02948718 0.8620432 0.7217143 0.8889360
0.2 0.03435897 0.8622358 0.7234958 0.8889428
0.2 0.03923077 0.8622652 0.7223361 0.8885859
0.2 0.04410256 0.8620627 0.7229412 0.8889495
0.2 0.04897436 0.8620116 0.7188235 0.8874949
0.2 0.05384615 0.8619238 0.7153109 0.8864040
0.2 0.05871795 0.8619083 0.7147227 0.8856700
0.2 0.06358974 0.8614219 0.7141513 0.8853064
0.2 0.06846154 0.8613769 0.7094790 0.8845724
0.2 0.07333333 0.8613267 0.7089076 0.8849360
0.2 0.07820513 0.8613301 0.7071429 0.8842088
0.2 0.08307692 0.8613348 0.7065546 0.8823906
0.2 0.08794872 0.8612440 0.7036471 0.8805724
0.2 0.09282051 0.8607954 0.7036639 0.8791111
0.2 0.09769231 0.8604293 0.7007563 0.8765657
0.2 0.10256410 0.8599402 0.7007563 0.8747475
0.2 0.10743590 0.8591161 0.7025378 0.8736431
0.2 0.11230769 0.8580974 0.7019496 0.8721818
0.2 0.11717949 0.8576971 0.7025042 0.8710774
0.2 0.12205128 0.8571679 0.7025042 0.8703502
0.2 0.12692308 0.8567174 0.7024874 0.8688956
0.2 0.13179487 0.8561708 0.7007395 0.8656229
0.2 0.13666667 0.8550908 0.7013277 0.8648956
0.2 0.14153846 0.8544119 0.7007395 0.8645320
0.2 0.14641026 0.8540218 0.7007563 0.8641684
0.2 0.15128205 0.8534856 0.7007563 0.8638047
0.2 0.15615385 0.8528506 0.7001681 0.8627138
0.2 0.16102564 0.8523328 0.6984034 0.8627138
0.2 0.16589744 0.8514521 0.6984034 0.8619865
0.2 0.17076923 0.8508972 0.6978151 0.8598047
0.2 0.17564103 0.8506594 0.6972269 0.8594411
0.2 0.18051282 0.8500216 0.6978151 0.8583434
0.2 0.18538462 0.8495585 0.6972437 0.8572458
0.2 0.19025641 0.8490212 0.6966723 0.8561549
0.2 0.19512821 0.8488122 0.6955126 0.8557912
0.2 0.20000000 0.8477981 0.6949244 0.8547003
0.4 0.01000000 0.8608629 0.7170924 0.8856835
0.4 0.01487179 0.8630875 0.7188235 0.8875017
0.4 0.01974359 0.8635522 0.7235126 0.8882290
0.4 0.02461538 0.8638852 0.7241008 0.8863973
0.4 0.02948718 0.8640961 0.7246387 0.8874882
0.4 0.03435897 0.8636815 0.7205882 0.8878519
0.4 0.03923077 0.8634645 0.7188403 0.8867542
0.4 0.04410256 0.8625804 0.7165210 0.8845589
0.4 0.04897436 0.8610777 0.7129916 0.8845589
0.4 0.05384615 0.8603462 0.7106218 0.8812727
0.4 0.05871795 0.8593766 0.7100168 0.8783569
0.4 0.06358974 0.8586905 0.7106387 0.8758047
0.4 0.06846154 0.8577189 0.7083193 0.8739865
0.4 0.07333333 0.8561190 0.7071765 0.8707138
0.4 0.07820513 0.8539432 0.7042521 0.8692525
0.4 0.08307692 0.8520797 0.7048571 0.8674276
0.4 0.08794872 0.8510941 0.7054454 0.8652391
0.4 0.09282051 0.8498438 0.7060168 0.8612391
0.4 0.09769231 0.8489821 0.7042521 0.8590572
0.4 0.10256410 0.8485292 0.7054286 0.8543232
0.4 0.10743590 0.8478570 0.7042521 0.8535960
0.4 0.11230769 0.8474843 0.7013277 0.8514074
0.4 0.11717949 0.8474952 0.7001345 0.8514141
0.4 0.12205128 0.8472912 0.6995462 0.8503232
0.4 0.12692308 0.8465502 0.6989580 0.8499596
0.4 0.13179487 0.8458615 0.6977983 0.8495960
0.4 0.13666667 0.8460465 0.6960336 0.8499596
0.4 0.14153846 0.8456487 0.6936807 0.8510640
0.4 0.14641026 0.8457106 0.6919328 0.8510640
0.4 0.15128205 0.8463243 0.6884034 0.8514276
0.4 0.15615385 0.8466792 0.6872269 0.8514276
0.4 0.16102564 0.8467969 0.6860840 0.8514276
0.4 0.16589744 0.8465443 0.6860840 0.8517912
0.4 0.17076923 0.8460047 0.6854958 0.8521549
0.4 0.17564103 0.8456889 0.6843193 0.8521549
0.4 0.18051282 0.8452321 0.6843193 0.8521549
0.4 0.18538462 0.8443937 0.6831765 0.8525185
0.4 0.19025641 0.8439948 0.6831765 0.8525185
0.4 0.19512821 0.8432182 0.6831765 0.8525185
0.4 0.20000000 0.8429886 0.6820000 0.8525185
0.6 0.01000000 0.8615146 0.7217311 0.8889428
0.6 0.01487179 0.8625244 0.7234118 0.8911380
0.6 0.01974359 0.8627385 0.7234622 0.8911246
0.6 0.02461538 0.8630850 0.7211765 0.8900202
0.6 0.02948718 0.8622451 0.7194286 0.8860000
0.6 0.03435897 0.8620347 0.7135966 0.8830842
0.6 0.03923077 0.8608737 0.7077311 0.8808956
0.6 0.04410256 0.8595263 0.7077647 0.8776229
0.6 0.04897436 0.8580028 0.7071765 0.8750774
0.6 0.05384615 0.8563695 0.7054454 0.8725253
0.6 0.05871795 0.8546962 0.7048739 0.8641616
0.6 0.06358974 0.8528297 0.7066050 0.8601549
0.6 0.06846154 0.8511820 0.7071765 0.8568822
0.6 0.07333333 0.8498130 0.7089244 0.8521481
0.6 0.07820513 0.8489291 0.7083361 0.8488687
0.6 0.08307692 0.8492258 0.7071597 0.8488620
0.6 0.08794872 0.8491236 0.7054118 0.8488620
0.6 0.09282051 0.8492666 0.7018824 0.8488620
0.6 0.09769231 0.8486469 0.7001176 0.8492256
0.6 0.10256410 0.8485608 0.6954790 0.8506869
0.6 0.10743590 0.8486213 0.6937143 0.8506869
0.6 0.11230769 0.8478946 0.6931261 0.8506869
0.6 0.11717949 0.8466370 0.6901849 0.8506869
0.6 0.12205128 0.8461776 0.6860840 0.8517912
0.6 0.12692308 0.8455451 0.6860840 0.8517912
0.6 0.13179487 0.8436514 0.6854958 0.8517912
0.6 0.13666667 0.8430642 0.6837647 0.8521549
0.6 0.14153846 0.8423842 0.6831765 0.8521549
0.6 0.14641026 0.8418510 0.6825882 0.8525185
0.6 0.15128205 0.8407780 0.6820000 0.8525185
0.6 0.15615385 0.8404541 0.6814118 0.8525185
0.6 0.16102564 0.8401007 0.6814118 0.8525185
0.6 0.16589744 0.8396466 0.6814118 0.8525185
0.6 0.17076923 0.8391832 0.6814118 0.8525185
0.6 0.17564103 0.8392284 0.6814118 0.8525185
0.6 0.18051282 0.8390840 0.6814118 0.8525185
0.6 0.18538462 0.8388220 0.6814118 0.8525185
0.6 0.19025641 0.8389099 0.6814118 0.8525185
0.6 0.19512821 0.8367532 0.6814118 0.8525185
0.6 0.20000000 0.8350971 0.6814118 0.8525185
[ reached getOption("max.print") -- omitted 80 rows ]
ROC was used to select the optimal model using the largest value.
The final values used for the model were alpha = 0.1 and lambda = 0.05871795.
plot(glmnetFit,plotType='level')plot(varImp(glmnetFit))densityplot(glmnetFit,pch='|')predict(glmnetFit,type = 'prob') -> train.glmnet.Probs
histogram(~Survived+Dead,train.glmnet.Probs)re <-
resamples(
x = list(
xgb = xgbFit,
xgbsmote = xgbsmoteFit,
rf = rfFit.y,
rfsmote = rfsmoteFit.y,
gbm = boostFit,
elastinet=glmnetFit
)
)
summary(re)
Call:
summary.resamples(object = re)
Models: xgb, xgbsmote, rf, rfsmote, gbm, elastinet
Number of resamples: 50
ROC
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
xgb 0.7360963 0.8603304 0.8925134 0.8839215 0.9196505 0.9540260 0
xgbsmote 0.7462567 0.8653743 0.8867647 0.8800014 0.9078877 0.9350267 0
rf 0.7807487 0.8622670 0.8984301 0.8885360 0.9126833 0.9556150 0
rfsmote 0.7759358 0.8581818 0.8880026 0.8831274 0.9106551 0.9574866 0
gbm 0.7604278 0.8570834 0.8848587 0.8817104 0.9055828 0.9609626 0
elastinet 0.7518717 0.8413102 0.8650822 0.8648624 0.9010538 0.9636364 0
Sens
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
xgb 0.5588235 0.7058824 0.7352941 0.7388908 0.7941176 0.9142857 0
xgbsmote 0.4411765 0.5882353 0.6470588 0.6496134 0.7058824 0.8529412 0
rf 0.5294118 0.6764706 0.7352941 0.7286891 0.7941176 0.8823529 0
rfsmote 0.5294118 0.6470588 0.6857143 0.6983025 0.7647059 0.9117647 0
gbm 0.5588235 0.6907563 0.7647059 0.7532269 0.8235294 0.9117647 0
elastinet 0.5294118 0.6470588 0.7390756 0.7205378 0.7714286 0.8529412 0
Spec
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
xgb 0.7777778 0.8709596 0.8898990 0.8844916 0.9090909 0.9636364 0
xgbsmote 0.7818182 0.8909091 0.9272727 0.9169630 0.9454545 1.0000000 0
rf 0.8181818 0.8727273 0.8909091 0.8940202 0.9259259 0.9818182 0
rfsmote 0.8181818 0.8727273 0.8909091 0.9016498 0.9272727 0.9818182 0
gbm 0.8181818 0.8545455 0.8898990 0.8849091 0.9217172 0.9818182 0
elastinet 0.8000000 0.8545455 0.8909091 0.8849360 0.9090909 0.9636364 0
bwplot(re)summary(diff(re))
Call:
summary.diff.resamples(object = diff(re))
p-value adjustment: bonferroni
Upper diagonal: estimates of the difference
Lower diagonal: p-value for H0: difference = 0
ROC
xgb xgbsmote rf rfsmote gbm elastinet
xgb 0.0039200 -0.0046145 0.0007941 0.0022111 0.0190591
xgbsmote 1.0000000 -0.0085345 -0.0031259 -0.0017090 0.0151390
rf 1.0000000 1.0000000 0.0054086 0.0068256 0.0236736
rfsmote 1.0000000 1.0000000 0.0511756 0.0014169 0.0182650
gbm 1.0000000 1.0000000 0.2458148 1.0000000 0.0168480
elastinet 0.7953846 1.0000000 2.206e-06 0.0007768 3.211e-05
Sens
xgb xgbsmote rf rfsmote gbm elastinet
xgb 0.089277 0.010202 0.040588 -0.014336 0.018353
xgbsmote 0.0001186 -0.079076 -0.048689 -0.103613 -0.070924
rf 1.0000000 0.0001066 0.030387 -0.024538 0.008151
rfsmote 0.4046800 0.0365380 0.0001694 -0.054924 -0.022235
gbm 1.0000000 1.143e-06 0.0174450 9.138e-07 0.032689
elastinet 1.0000000 0.0036014 1.0000000 0.4417908 3.796e-05
Spec
xgb xgbsmote rf rfsmote gbm elastinet
xgb -3.247e-02 -9.529e-03 -1.716e-02 -4.175e-04 -4.444e-04
xgbsmote 0.014850 2.294e-02 1.531e-02 3.205e-02 3.203e-02
rf 1.000000 0.436352 -7.630e-03 9.111e-03 9.084e-03
rfsmote 0.503523 1.000000 0.229579 1.674e-02 1.671e-02
gbm 1.000000 0.027924 0.354081 0.007644 -2.694e-05
elastinet 1.000000 0.029234 1.000000 0.068587 1.000000
simulatedTrain <- data.frame(Class = train.imp$Survived)
simulatedTrain$rf = predict(rfFit.y,type = 'prob')[[1]]
simulatedTrain$rfsmote = predict(rfsmoteFit.y,type = 'prob')[[1]]
simulatedTrain$xgb = predict(xgbFit,type = 'prob')[[1]]
simulatedTrain$xgbsmote = predict(xgbsmoteFit,type = 'prob')[[1]]
simulatedTrain$boost = predict(boostFit,type = 'prob')[[1]]calCurve <- calibration(x = Class~rf+rfsmote+xgb+xgbsmote+boost,data = simulatedTrain)
xyplot(calCurve,auto.key=list(columns=3))boostsigmoidCal <- glm(relevel(Class,ref='Dead')~boost,simulatedTrain,family = 'binomial')
coef(summary(boostsigmoidCal)) Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.393991 0.1992172 -17.03664 4.392812e-65
boost 7.063846 0.4064620 17.37886 1.193056e-67
simulatedTrain$boostSig = predict(boostsigmoidCal,type = 'response')
xgbsmotesigmoidCal <- glm(relevel(Class,ref='Dead')~xgbsmote,simulatedTrain,family = 'binomial')
coef(summary(xgbsmotesigmoidCal)) Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.468413 0.2016514 -17.20004 2.653386e-66
xgbsmote 9.205386 0.5945264 15.48356 4.479596e-54
simulatedTrain$xgbsmoteSig = predict(xgbsmotesigmoidCal,type = 'response')
calibration(x = Class~boost+boostSig,data = simulatedTrain) %>%
xyplot(auto.key=list(columns=2))calibration(x = Class~xgbsmote+xgbsmoteSig,data = simulatedTrain) %>%
xyplot(auto.key=list(columns=2))test.imp <- test.raw
#Embarked
test.imp$Embarked[is.na(test.imp$Embarked)]='S'
#Title
test.raw$title <- str_extract(pattern = '[a-zA-Z]+(?=\\.)',string = test.raw$Name)
#test.raw$title <- as.factor(test.raw$title)
test.imp$title <- as.character(test.raw$title)
test.imp$title[test.imp$title %in% c('Capt','Col','Major')] <- 'Officer'
test.imp$title[test.imp$title %in% c('Don','Dr','Rev','Sir','Jonkheer','Countess','Lady','Dona')] <- 'Royalty'
test.imp$title[test.imp$title %in% c('Mrs','Mme')] <- 'Mrs'
test.imp$title[test.imp$title %in% c('Ms','Mlle')] <- 'Miss'
test.imp$title <- as.factor(test.imp$title)
#Missing age
missing.age <- test.imp %>% filter(is.na(Age))
age.predicted <- predict(rfFit, newdata = missing.age)
test.imp$Age[is.na(test.imp$Age)] <- age.predicted
test.imp$Age[test.imp$title=='Master' & test.imp$Age > 20] <- 4
#Child
test.imp$child <- 0
test.imp$child[test.imp$Age<18] <- 1
test.imp$almostadult <- as.numeric(between(test.imp$Age,16,18))
#Young/old
test.imp$Young <- ifelse(test.imp$Age<10,1,0)
test.imp$Seniors <- ifelse(test.imp$Age>60,1,0)
#Family Related
test.imp$TotalFam <- test.imp$SibSp + test.imp$Parch + 1
test.imp$Name <- NULL
test.imp$LargeParCh <- as.numeric(test.imp$Parch>=3)
test.imp$LargeSibSp <- as.numeric(test.imp$SibSp>=3)
test.imp$Single <- ifelse(test.imp$TotalFam==1,1,0)
test.imp$Couple <- ifelse(test.imp$TotalFam==2,1,0)
test.imp$Family <- ifelse(test.imp$TotalFam>2,1,0)
#Cabin & Deck
test.imp$CabinMissing <- as.numeric(is.na(test.raw$Cabin))
test.imp$CabinCode <- map_chr(test.raw$Cabin,~str_split(string = .x,pattern = '')[[1]][1])
test.imp$CabinCode[is.na(test.imp$CabinCode)] <- 'U'
test.imp$CabinNum <- as.numeric(map_chr(test.raw$Cabin,~str_split(string = .x,pattern = '[a-zA-Z]')[[1]][2]))
test.imp$CabinNum <- map_int(test.imp$CabinNum, ~as.integer(str_split(.x,pattern = '',simplify = T)[1][1]))
test.imp$CabinNum[is.na(test.imp$CabinNum)] <- 0
test.imp$CabinCode <- factor(
x = test.imp$CabinCode,
levels = unique(train.imp$CabinCode)
)
test.imp$TopDeck <- ifelse(test.imp$CabinCode %in% c('A','B'),1,0)
test.imp$MidDeck <- ifelse(test.imp$CabinCode %in% c('C','D'),1,0)
test.imp$LowerDeck <- ifelse(test.imp$TopDeck==0 & test.imp$MidDeck ==0 ,1,0)
test.imp$NumberofCabins <- map_int(test.raw$Cabin,~str_split(string = .x,pattern = ' ')[[1]] %>% length)
test.imp$Cabin <- NULL
# Ticket
test.imp %<>%
mutate(
Ticket = str_to_upper(Ticket) %>%
str_replace_all(pattern = regex(pattern = '[.\\/]'),replacement = ''),
TicketNum = str_extract(Ticket,pattern = regex('([0-9]){3,}')),
TicketNumStart = map_int(TicketNum,~as.integer(str_split(.x,pattern = '',simplify = T)[1])),
TicketNumLen = map_int(TicketNum,~dim(str_split(.x,pattern = '',simplify = T))[2]),
TicketChar = str_extract(Ticket,pattern = regex('^[a-zA-Z/\\.]+'))
) %>%
mutate(
TicketChar = map_chr(.x=TicketChar,
.f=~str_split(string=.x, pattern = '',simplify = T)[1])
) %>%
mutate(
TicketChar = ifelse(is.na(TicketChar),'U',TicketChar),
TicketNumStart = ifelse(is.na(TicketNumStart),0,TicketNumStart),
TicketNumLen = ifelse(is.na(TicketNumLen),0,TicketNumLen),
)
test.imp$Ticket <- NULL
test.imp$TicketNum <- NULL
#Fare
test.imp$Fare[is.na(test.imp$Fare)] <- 14.4542
test.imp$Fare[test.imp$Fare>232] <- 232xgbsmotesigmoidCal
Call: glm(formula = relevel(Class, ref = "Dead") ~ xgbsmote, family = "binomial",
data = simulatedTrain)
Coefficients:
(Intercept) xgbsmote
-3.468 9.205
Degrees of Freedom: 890 Total (i.e. Null); 889 Residual
Null Deviance: 1187
Residual Deviance: 491.9 AIC: 495.9
PID <-
readData(Titanic.path,
test.data.file,
test.column.types,
missing.types)
PID <- PID$PassengerIdfor(m in ls(pattern = 'Pred')) {
write.csv(
x = data.frame(
PassengerId = PID,
Survived = as.numeric(eval(parse(text = m))) * -1 + 2
),
file = paste0(m,'.csv'),
row.names = F
)
}I think this approach depends on the academic background and the industry of the analyst. Prof Srinivasan, and my mentor at work both have strong statistical academic backgrounds, and both believe in thorough EDA of the data. I’ve also noticed this approach from individuals in the banking & insurance industry - perhaps due to regulatory requirements. On the other hand, folks trained in computer science and algorithmic data science tend to underplay the importance of thorough EDA.↩
To iterate variable names in ggplot, use ggplot(...)+aes_string(...) in place of ggplot(...,aes(...)).↩
Read more about beanplots here: https://cran.r-project.org/web/packages/beanplot/vignettes/beanplot.pdf↩